Estimating apparatus

ABSTRACT

In a travelling road estimating apparatus, an estimator estimates, based on the coordinates of at least one of edge points included in a selected candidate, a road parameter using a previously prepared filter having an adjustable response level. The road parameter is associated with a condition of the travelling road relative to the vehicle and a shape of the travelling road. A determiner determines whether there is an unstable situation that causes an accuracy of estimating the edge points by an edge extractor to be reduced. A response level adjuster adjusts the response level of the filter in accordance with determination of whether there is an unstable situation that causes an accuracy of estimating the edge points by the edge extractor to be reduced.

CROSS REFERENCE TO RELATED APPLICATION

This application is based on and claims the benefit of priority fromJapanese Patent Applications No. 2017-005349 filed on Jan. 16, 2017, thedisclosure of which is incorporated in its entirety herein by reference.

TECHNICAL FIELD

The present disclosure relates to technologies for recognizing lanemarking lines, i.e. lane boundary lines, from an image.

BACKGROUND

One of conventional technologies extracts edge points in traffic lanelines on a road from an image obtained from a camera installed to avehicle, and obtains the coordinates of each of the edge points.

Then, the conventional technology inputs the obtained coordinates ofeach of the edge points as observations of a corresponding one ofvariables to a previously prepared filter, such as a Kalman filter.Then, the filter estimates state values of a road, such as values ofroad parameters, which include, for example, a lateral offset position,a yaw angle, and a curvature of the road relative to the travellingdirection of the vehicle, i.e. the optical axis of the camera.

The filter has a controllable response to the observations inputthereto. For example, the filter sets a level of the response to ahigher level upon the vehicle travelling or is about to travel on acurved road to thereby prevent response lag to the input of theobservations. In contrast, the filter sets a level of the response to alower level upon the vehicle travelling on a straight road to therebyprevent the vehicle from zig-zaging.

Japanese patent application publication No. 2006-285493, which will bereferred to as a published patent document, discloses the followingtechnology. Specifically, the disclosed technology changes the responseof a filter, which is installed in a vehicle, depending on the curvatureof the road on which the vehicle is travelling; the curvature isestimated based on a yaw rate of the vehicle observed as an observation.

SUMMARY

As a result of detailed consideration of the published patent document,the inventors of this application have found the following issue.Specifically, changing the response of the filter in accordance with theyaw rate of the vehicle may result in delay of the timing at which alevel of the response of the filter is actually changed to a properlevel suitable for the environmental circumstances around the vehicle.

Specifically, the yaw rate of the vehicle is measured after the vehiclehas entered a curve in a road. For this reason, changing a level of theresponse of the filter in accordance with the yaw rate of the vehiclemay result in delay of the timing at which a level of the response ofthe filter is actually changed to a proper level suitable for the curve.

For improving the response of the filter upon the vehicle entering acurve in a road, let us consider a measure to set a higher level of theresponse of the filter upon the vehicle travelling on a straight sectionof the road before the vehicle entering a curve.

The inventors of this application however have found the followingadditional issue described hereinafter.

Specifically, if there are faded colors of making lines on a road,cracks on the road, and/or coal-tar repairs on the road, these factorsmay reduce the extraction accuracy of the edge points. For this reason,the above measure may result in higher response to these lower-accuracyedge points. This may result in reduction in the stability of estimatingthe state values of the road, causing, for example, lane keeping assistcontrol based on the estimated state values of the road to becomeunstable.

The present disclosure provides technologies that balance bothsufficient response of a previously prepared filter and stableestimation of a travelling road of a vehicle.

According to a first exemplary aspect of the present disclosure, thereis provided a travelling road estimating apparatus. The travelling roadestimating apparatus includes an edge extractor configured to extractedge points from an image of a travelling road ahead of a vehiclecaptured by an image capturing unit of the vehicle, and calculatecoordinates of each of the edge points, each of the edge pointsrepresenting an extracted pixel of the image. The extracted pixel has aluminance level higher by at least a predetermined threshold level thana luminance level of at least one of pixels of the image adjacent to theextracted pixel. The travelling road estimating apparatus includes acandidate extractor configured to extract, based on the coordinates ofeach of the edge points, at least one line candidate that is a candidateof a lane marking line of the travelling road, and a selector configuredto select the at least one line candidate as a selected candidate. Thetravelling road estimating apparatus includes an estimator configured toestimate, based on the coordinates of at least one of the edge pointsincluded in the selected candidate, a road parameter using a previouslyprepared filter having an adjustable response level. The road parameteris associated with a condition of the travelling road relative to thevehicle and a shape of the travelling road. The travelling roadestimating apparatus includes a determiner configured to determinewhether there is an unstable situation that causes an accuracy ofestimating the edge points by the edge extractor to be reduced. Thetravelling road estimating apparatus includes a response level adjusterconfigured to adjust the response level of the filter in accordance withdetermination of whether there is an unstable situation that causes anaccuracy of estimating the edge points by the edge extractor to bereduced.

According to a second exemplary aspect of the present disclosure, thereis provided a travelling road estimating method. The travelling roadestimating method includes extracting edge points from an image of atravelling road ahead of a vehicle captured by an image capturing unitof the vehicle, and calculating coordinates of each of the edge points,each of the edge points representing an extracted pixel of the image.The extracted pixel has a luminance level higher by at least apredetermined threshold level than a luminance level of at least one ofpixels of the image adjacent to the extracted pixel. The travelling roadestimating method extracts, based on the coordinates of each of the edgepoints, at least one line candidate that is a candidate of a lanemarking line of the travelling road. The travelling road estimatingmethod includes selecting the at least one line candidate as a selectedcandidate, and estimating, based on the coordinates of at least one ofthe edge points included in the selected candidate, a road parameterusing a previously prepared filter having an adjustable response level.The road parameter is associated with a condition of the travelling roadrelative to the vehicle and a shape of the travelling road. Thetravelling road estimating method includes determining whether there isan unstable situation that causes an accuracy of estimating the edgepoints by the extracting step to be reduced. The travelling roadestimating method includes adjusting the response level of the filter inaccordance with determination of whether there is an unstable situationthat causes an accuracy of estimating the edge points by the edge-pointextracting step to be reduced.

Each of the first and second exemplary aspects enables the responselevel of the filter to be sufficiently maintained until it is determinedthat there is an unstable situation that causes the accuracy ofextracting the edges to be reduced. This configuration ensures thesufficiently maintained response level of the filter independently ofwhether the vehicle is travelling toward a straight section of a road ora curved section in the road.

BRIEF DESCRIPTION OF THE DRAWINGS

Other aspects of the present disclosure will become apparent from thefollowing description of embodiments with reference to the accompanyingdrawings in which:

FIG. 1 is a block diagram schematically illustrating an example of theoverall structure of a cruise assist system according to an exemplaryembodiment of the present disclosure;

FIG. 2 is a schematic plan and side view of a vehicle illustrated inFIG. 1 for illustrating an imaging region of an image capturing unitillustrated in FIG. 1;

FIG. 3 is a block diagram schematically illustrating a functionalstructure of a road estimating apparatus illustrated in FIG. 1;

FIG. 4A is a view schematically illustrating some of road parametersaccording to the exemplary embodiment;

FIG. 4B is a view schematically illustrating some of feature informationitems generated by a feature information generator illustrated in FIG.3;

FIG. 5 is a flowchart schematically illustrating a response adjustmentroutine carried out by a response level determiner of the roadestimating apparatus illustrated in FIG. 3;

FIG. 6A is a view schematically illustrating an example of an allowablerange established around the outline of a selected boundary linecandidate;

FIG. 6B is a view schematically illustrating a forward image captured byan image capturing unit;

FIG. 7 is a graph schematically illustrating a luminance leveldistribution of the image on a horizontal pixel line illustrated in FIG.6B;

FIG. 8 is a view schematically illustrating a forward image captured bythe image capturing unit if there is a travelling situation of an ownvehicle passing through a shade region under an elevated structure;

FIG. 9A is a view schematically illustrating a group of coal-tar linesextending along a lane boundary line to be adjacent to the lane boundaryline;

FIG. 9B is a view schematically illustrating a group of tire tracesextending along a lane boundary line to be adjacent to the lane boundaryline;

FIG. 10 is a graph schematically illustrating a first result of acurvature of a straight road including cracks thereon estimated by aconventional technology disclosed in the published patent document usinga dashed line, and a second result of the curvature of the same roadestimated by the road estimating apparatus while the own vehicle istravelling on the same road using a solid line; and

FIG. 11 is a view schematically illustrating a situation where there aremany edge points, such as coal-tar lines, in the allowable rangepreviously determined for a selected boundary line candidate.

DETAILED DESCRIPTION OF EMBODIMENT

The following describes an exemplary embodiment of the presentdisclosure with reference to the accompanying drawings.

Overall Structure

The following describes an example of the overall structure of a cruiseassist system 1 according to the exemplary embodiment.

Referring to FIG. 1, the cruise assist system 1 is installed in avehicle; the vehicle will be referred to as an own vehicle V. The cruiseassist system 1 is configured to estimate values of predetermined roadparameters from a forward image; the forward image is an image of aforward region of the own vehicle V captured by an image capturing unit2 described later. Then, the cruise assist system 1 is configured toperform various cruise assist tasks in accordance with the estimatedvalues of the road parameters. The road parameters represent theconditions of a travelling road, such as a travelling course, upon whichthe own vehicle V is travelling and the shape of the travelling road.The detailed descriptions of the road parameters will be describedlater.

The cruise assist system 1 includes an image capturing unit 2, sensors3, a road estimating apparatus 4, and a cruise-assist executing unit 5.

The image capturing unit 2 is installed in the own vehicle V to capturea forward portion of a road on which the own vehicle V is located. Theimage capturing unit 2 is mounted to, for example, the front side of thetop of the own vehicle V. In detail, the image capturing unit 2 ismounted to the center of the front side of the own-vehicle's top in itswidth direction. The image capturing unit 2 has a predetermined imagingregion R that horizontally and vertically expands therefrom in the frontdirection of the own vehicle V to have a substantially sector or asubstantially semi-circular shape in the front direction of the ownvehicle V. That is, the image capturing unit 2 captures images of theimaging region R. For example, the image capturing unit 2 is comprisedof a CCD camera, a CMOS image sensor, or a near-infrared camera, andconfigured to capture color images of the imaging region R using acontrolled exposure time, i.e. a controlled shatter speed; each of thecolor images includes a luminance level and color information of eachpixel thereof.

The sensors 3 are installed in the own vehicle V and are each configuredto measure a corresponding vehicle parameter indicative of theconditions of the own vehicle V and/or the behavior of the own vehicleV. Each of the sensors 3 is also configured to output, to the roadestimating apparatus 4, a measurement value of the corresponding vehicleparameter. For example, the sensors 3 include a vehicle speed sensorconfigured to measure a speed of the own vehicle V as a function of, forexample, the rotational speed of each wheel of the own vehicle V.

The sensors 3 can include a yaw-rate sensor that measures an angularvelocity, i.e. angular rate, about the vertical direction of the ownvehicle V, and a global-positioning-system (GPS) sensor that measuresthe position of the own vehicle V. The sensors 3 can also include aradar sensor that detects objects located around the own vehicle V usingradar waves, and obtains distances from the own vehicle V to thedetected objects and relative speeds of the detected objects relative tothe own vehicle V.

The road estimating apparatus 4 is centered on a known type ofmicrocomputer, equipped with a CPU 4 a and a memory 4 b comprised of atleast a ROM, a RAM, and/or a semiconductor memory such as a flashmemory. The road estimating apparatus 4 also includes an I/O device 4 cconnected via input ports to the image capturing unit 2 and the sensors3 and connected via an output port to the cruise-assist executing unit5. The various functions of the road estimating apparatus 4 areimplemented by the CPU 4 a in executing programs that are stored innon-transitory storage media. With this embodiment, the memory 4 bcorresponds to the non-transitory storage media in which the programsare stored. Furthermore, the CPU 4 a executes the programs, thusexecuting methods corresponding to the programs. The road estimatingapparatus 4 is not necessarily configured with a single microcomputer,and it would be equally possible to have a plurality of microcomputers.

The road estimating apparatus 4 executes at least a road recognitionroutine, i.e. a road estimation routine, in accordance with

(1) The images captured by the image capturing unit 2 and receivedtherefrom via the corresponding input port of the I/O unit 4 c

(2) The measurement values of the vehicle parameters measured by therespective sensors 3 and received therefrom via the corresponding inputports of the I/O unit 4 c

The cruise-assist executing unit 5 is configured to receive a result ofthe road recognition routine, i.e. an estimated travelling road of theown vehicle V, from the road estimating apparatus 4 and execute, basedon the result of the road recognition routine, various cruise-assistcontrol tasks, which includes a lane keeping assist task, to activatevarious types of controlled target devices TD. For example, thecontrolled target devices TD include actuators AC and a warning deviceWD.

The actuators AC include a first actuator for driving a brake device BDinstalled in the own vehicle V, a second actuator for rotating asteering wheel SW of the own vehicle V for assisting the driver'sturning operation of the steering wheel, and a third actuator fortightening a seat belt SB mounted to each seat of the own vehicle V. Thewarning device WD includes a speaker and/or a display mounted in thecompartment of the own vehicle V, and outputs warnings including, forexample, warning sounds and/or warning messages for the driver.

Operation

The road estimating apparatus 4 is configured to run corresponding oneor more programs to thereby implement a road recognition function.

Referring to FIG. 3, the road estimating apparatus 4 includes, ascomponents that implement the road recognition function, an imageobtaining unit 41, a sensor information obtaining unit 42, an ambientinformation obtaining unit 43, an edge extractor 44, a candidateextractor 45, an information extractor 46, a response level determiner47, a selector 48, and an estimator 49.

The method of implementing the function of each component 41 to 49 isnot necessarily limited to be based on software, and it would be equallypossible for all or part of the function to be realized by using one ormore hardware elements. For example, in a case in which the abovefunctions are realized through use of hardware comprised of electroniccircuitry, the electronic circuitry could be implemented as a digitalcircuit containing a plurality of logic circuits, or as analog circuits,or as a combination of these.

The image obtaining unit 41 obtains a captured image from the imagecapturing unit 2 in a predetermined cycle; this cycle will be referredto as a processing cycle. In other words, the image capturing unit 2captures an image, i.e. a forward image, in the processing cycle, andthe image obtaining unit 41 obtains the image each time the image iscaptured by the image capturing unit 2 in the processing cycle.

The sensor information obtaining unit 42 obtains, from the sensors 3,the measurement values of the vehicle parameters measured by therespective sensors 3. In particular, the sensor information obtainingunit 42 according to this embodiment obtains the speed and the yaw rateof the own vehicle V from the corresponding vehicle speed sensor and theyaw-rate sensor.

The ambient information obtaining unit 43 is configured to obtain theluminance level around the road in front of the own vehicle V in eachprocessing cycle, and obtain, as luminance change information, anabsolute value of the luminance deviation ΔC, which will be referred toas a luminance deviation ΔC, of the luminance level obtained by thecurrent processing cycle from the luminance level obtained by the lastprevious processing cycle. For example, the ambient informationobtaining unit 43 calculates the average value of the luminance levelsof all the pixels of the image obtained by the image obtaining unit 41in each processing cycle, thus obtaining the average value as theluminance level around the road in front of the own vehicle V. Asanother example, the ambient information obtaining unit 43 can obtainthe luminance level around the road in front of the own vehicle V ineach processing cycle using a luminance sensor.

The edge extractor 44 applies a known edge extraction filter, such as aSobel filter, on the image obtained by the image obtainer 41 to therebyextract edge points from the image. Each edge point represents acorresponding point, i.e. a corresponding pixel, having a luminancelevel higher by at least a predetermined threshold level than theluminance level of at least one of pixels of the image adjacent to thecorresponding pixel.

The candidate extractor 45 is configured to perform, for example, Houghtransform of the extracted edge points. The Hough transform is capableof extracting, based on the extracted edge points, line candidates onwhich some of the edge points are aligned. Each of the line candidatesrepresents a candidate of lines each of which represents a boundarybetween a corresponding one of marking lines painted on the road and theroad surface. The marking lines are painted to show the range of thetravelling road on which vehicles can travel.

The selector 48 is configured to select, from the extracted linecandidates, a pair of line candidates respectively represents right andleft lane boundary lines, i.e. right and left lane marking lines, of thetravelling road on which the own vehicle V is travelling. The selectedpair of line candidates will be referred to as selected boundary linecandidates. For example, the selector 48 can obtain, from the extractedline candidates, the selected boundary line candidates in accordancewith various pieces of information extracted from the informationextractor 46, which are described later, and/or the information abouteach of the extracted line candidates.

The estimator 49 is configured to obtain, as observations, coordinatesof the edge points constituting each of the selected boundary linecandidates, i.e. the selected right and left boundary line candidates.Then, the estimator 49 includes a Kalman filter, i.e. a Kalman filteralgorithm, KF stored in the memory 4 b, and applies the Kalman filter KFon the observations to thereby calculate the road parametersrepresenting the conditions of the travelling road, upon which the ownvehicle V is travelling, relative to the own vehicle V and the shape ofthe travelling road.

For example, the actual observations (measurements) in the currentprocessing cycle is referred to as z_(k), a priori state estimate foreach road parameter based on a predetermined state estimate model of thetravelling road is referred to as x _(k) m×n matrix H relates to thevariables of the road state model to the observations z_(k). A predictedobservations Hx _(k) based on the priori estimate x _(k) is referred toas Hx _(k). A posteriori state estimate for each road parameter isreferred to as {circumflex over (x)}_(k).

Then, the posteriori state estimate {circumflex over (x)}_(k) for eachroad parameter can be expressed by the following equation (1):

{circumflex over (x)} _(k) =x _(k) +K(z _(k) −Hx _(k))  (1)

Where K is an m×n matrix referred to as Kalman gain.

The estimator 49 outputs the calculated road parameters to the assistexecuting unit 5.

Note that the road parameters include, as parameters representing theconditions of the travelling road relative to the own vehicle V, anoffset yc, a lane inclination ϕ, and a pitching quantity β. In addition,the road parameters also include, as parameters representing the shapeof the travelling road, a curvature p and a lane width WI (see FIG. 4A).

The offset yc represents the minimum distance between

1. A line, which is referred to as a vehicle center line, extending fromthe image capturing unit 2 in the travelling direction of the ownvehicle V while centering around the image capturing unit 2

2. A center line, which is referred to as a lane center line, of thetravelling lane centered in the width direction of the travelling lane.Note that the minimum distance between the vehicle center line and thelane center line are measured when the vehicle center line and the lanecenter line have a predetermined identical height.

That is, the offset yc represents a value of displacement of the ownvehicle V in the width direction of the own vehicle V; the widthdirection of the own vehicle V will be referred to as a vehicle widthdirection.

For example, if the own vehicle V is travelling while the vehicle centerline is aligned with the lane center line, the offset yc becomes zero.

The lane inclination ϕ represents an inclined angle of a median linerelative to the travelling direction of the own vehicle V; the medianline is defined as a phantom line passing through an intermediate linebetween the right and left boundary lines, i.e. the median line ismatched with the lane center line. In other words, the lane inclinationϕ represents a yaw angle of the own vehicle V.

The pitching quantity β represents a pitching angle of the own vehicle Vrelative to the travelling road.

The curvature ρ represents a curvature of the median line.

The lane width WI represents an interval between the right and leftboundary lines in the direction, i.e. vehicle width direction,perpendicular to the vehicle center line of the own vehicle V.

Note that how the course estimating apparatus 4 estimates values of theroad parameters set forth above is disclosed in, for example, JapanesePatent Application Publication No. 2015-199423. The disclosure ofJapanese Patent Application Publication No. 2015-199423 is incorporatedentirely herein by reference.

The information extractor 46 is configured to generate various types ofinformation indicative of features of a marking line identified fromeach of the line candidates extracted by the candidate extractor 45. Forexample, the information extractor 46 includes a line type identifier461, a feature generator 462, a variation generator 463, and a deviationgenerator 464, and a reliability calculator 465.

The line type identifier 461 is configured to identify the type of amarking line indicated by each of the line candidates in accordance withthe edge points included in the corresponding one of the linecandidates. For example, the line type identifier 461 is configured toidentify the type of a marking line indicated by each of the linecandidates in accordance with the distribution of the edge pointsincluded in the corresponding one of the line candidates. For example,the type identifier 461 identifies whether the marking line shown byeach of the line candidates is a solid line or a dashed line, and/orwhether the marking line shown by each of the line candidates is asingle line or a part of multiple lines.

The feature generator 462 is configured to generate feature informationindicative of features of the marking line specified by each of the linecandidates. For example, the feature information for each of the markinglines specified by the respective line candidates include, as featureinformation items, a segment length L, a segment width W, an entire linelength AL, a road-surface luminance contrast, and a black boundaryluminance contrast.

Referring to FIG. 4, let us assume that the marking line specified by aselected one line candidate from the extracted line candidates is adashed line comprised of m line segments, i.e. m line blocks, B₁, B₂, .. . , B_(m), which are aligned with intervals therebetween along thetravelling road, where m is an integer equal to or more than 1. FIG. 4illustrates four line segments, B₁, B₂, B₃, and B₄ as an example of themarking line.

The segment length L includes longitudinal lengths L₁ to L_(m) of therespective line segments B₁ to B_(m). If the marking line specified bythe selected lane candidate is a solid line, i.e. is comprised of asingle segment, upon m being 1, the segment length L is equal to theentire line length AL described later.

The segment interval W includes the lengths of intervals between therespective adjacent line segments B₁ to B_(m). That is, the segmentinterval W includes the length of the interval W₁ between the adjacentline segments B₁ and B₂, the length of the interval W₂ between theadjacent line segments B₂ and B₃, . . . , and the length of the intervalW_(m-1) between the adjacent line segments B_(m-1) and B_(m). If themarking line specified by the selected lane candidate is a solid line,i.e. is comprised of a single segment, upon m being 1, the segmentinterval W is not generated.

The entire line length AL includes the length between the closest edgepoint and the farthest edge point in the edge points contained in theselected boundary line candidate; the closest edge point is the closestto the own vehicle V, and the farthest edge point is the farthest to theown vehicle V. If the marking line specified by the selected lanecandidate is a solid line, i.e. is comprised of a single segment, upon mbeing 1, the entire line length AL is substantially set to thelongitudinal length of the single segment.

Each of values P₁ to P_(m) of the road-surface luminance contrast of therespective line segments B₁ to B_(m) represents a contrast ratio of theluminance level or color of the corresponding line segment to apredetermined reference luminance level or color. Note that each of thevalues P₁ to P_(m) of the road-surface luminance contrast of therespective line segments B₁ to B_(m) is normalized to be within therange from 0 to 1 inclusive. That is, the higher the luminance level ofa line segment B_(i) is, the closer the value P_(i) of the road-surfaceluminance contrast is to 1 where reference character i represents anyone of the line segments B₁ to B_(m). In other words, the higher thecontrast ratio of the luminance level of the line segment B_(i) to thereference luminance level is, the closer the value P_(i) of theroad-surface luminance contrast is to 1.

Note that the reference luminance level is, for example, an averageluminance level of the luminance levels of pixels of the road surfaceextracted from the captured image, and the reference luminance color is,for example, an average color of the colors of the road surfaceextracted from the captured image.

Each of values Q₁ to Q_(m) of the black boundary luminance contrast ofthe respective line segments B₁ to B_(m) represents a contrast ratio ofthe luminance level or color of the road surface located at the boundaryof the corresponding line segment to the predetermined referenceluminance level or color. Note that each of the values Q₁ to Q_(m) ofthe black boundary luminance contrast of the respective line segments B₁to B_(m) is normalized to be within the range from 0 to 1 inclusive.That is, the higher the luminance level of the road surface located atthe boundary of a line segment B_(i) is, the closer the value Q_(i) ofthe black boundary luminance contrast is to 1. In other words, the lowerthe contrast ratio of the luminance level of the road surface located atthe boundary of the line segment B_(i) to the reference luminance levelis, the closer the value Q_(i) of the black boundary luminance contrastis to 1.

The variation generator 463 is configured to generate variationinformation indicative of the variations of values of at least one ofthe feature information items generated by the feature generator 462 inthe current processing cycle. For example, the variance generator 463 isconfigured to generate, as variation information items, the average PLand variance VL of the segment lengths L₁ to L_(m), and generate theaverage PW and variance VW of the segment intervals W₁ to WL_(m). Notethat the variation generator 463 is configured to generate the variationinformation only if the marking line specified by a selected boundaryline candidate includes line segments set forth above.

The deviation generator 464 is configured to generate

1. The deviation of the value(s) of at least one of the featureinformation items generated by the feature generator 462 in the currentprocessing cycle from the value(s) of the corresponding at least one ofthe feature information items generated by the feature generator 462 inthe last previous processing cycle

2. The deviation of the values of at least one of the variationinformation items generated by the variation generator 463 in thecurrent processing cycle from the values of the corresponding at leastone of the variation information items generated by the variationgenerator 463 in the last previous processing cycle

For example, the deviation generator 464 is configured to

1. Generate an absolute value of the deviation ΔAL, which will bereferred to as a deviation ΔAL, of the entire length AL generated by thefeature generator 462 in the current processing cycle from the entirelength AL generated by the feature generator 462 in the last previousprocessing cycle

2. Generate an absolute value of the deviation ΔPL, which will bereferred to as a deviation ΔPL, of the average PL generated by thevariation generator 463 in the current processing cycle from the averagePL generated by the variation generator 463 in the last previousprocessing cycle

3. Generate an absolute value of the deviation ΔPW, which will bereferred to as a deviation ΔPW, of the average PW generated by thevariation generator 463 in the current processing cycle from the averagePW generated by the variation generator 463 in the last previousprocessing cycle

In addition, the deviation generator 464 is configured to set a linetype identification flag to 1 if the type of the marking line indicatedby at least one boundary line candidate in the last previous processingcycle has been changed to another type in the current processing cycle.

The reliability calculator 465 is configured to generate a firstreliability DR and a second reliability DB for each of the line segmentsB_(i). Each of the first and second reliabilities DR and DB takes avalue being higher as the corresponding line segment B_(i) is moresimilar to a white line.

For example, the reliability calculator 465 calculates the firstreliability DR for each of the line segments B_(i) as a function of thesegment length L_(i) and the value P_(i) of the road-surface luminancecontrast in accordance with the following equation (2):

$\begin{matrix}{{DR} = \frac{\sum\limits_{i}\left( {P_{i} \times L_{i}} \right)}{\sum\limits_{i}L_{i}}} & (2)\end{matrix}$

Note that, if the marking line specified by a boundary line candidate iscomprised of a single segment B₁, the value P₁ of the road-surfaceluminance contrast of the line segment B₁ is calculated as the firstreliability DR of the marking line (line segment B₁).

In addition, the reliability calculator 465 for example calculates thesecond reliability DB for each of the line segments B_(i) as a functionof the segment length L_(i) and the value Q_(i) of the black boundaryluminance contrast in accordance with the following equation (3):

$\begin{matrix}{{DB} = \frac{\sum\limits_{i}\left( {Q_{i} \times L_{i}} \right)}{\sum\limits_{i}L_{i}}} & (3)\end{matrix}$

Note that, if the marking line specified by a boundary line candidate iscomprised of a single segment B₁, the value Q₁ of the black boundaryluminance contrast of the line segment B₁ is calculated as the secondreliability DB of the marking line (line segment B₁).

The response level determiner 47 is configured to perform a responseadjustment routine that adjusts the response level of the Kalman filterKF in accordance with the various types of information related to themarking line specified from each of the boundary line candidates, whichare extracted by the information extractor 46.

The Kalman filter KF, which is expressed by the equation (1), has theKalman gain K, and the response level of the Kalman filter KF isdetermined based on the Kalman gain K The Kalman gain K represents afactor representing which of the actual observations z_(k) in thecurrent processing cycle and the predicted observations Hx _(k) based onthe priori estimate x _(k) is more reliable than the other thereof.

That is, adjustment of the Kalman gain K to reduce the contribution ofthe observation z_(k) in the equation (1) enables the stability of theKalman filter KF to be improved while the response level of the Kalmanfilter KF is reduced. In contrast, adjustment of the Kalman gain K toreduce the contribution of the priori estimate x _(k) in the equation(1) enables the response level of the Kalman filter KF to be improvedwhile the stability of the Kalman filter KF is reduced.

For example, the response level determiner 47 is configured to adjustthe Kalman gain K, i.e. the response level of the Kalman filter KF, byadjusting a measurement error covariance calculated based on theequation (1).

Next, the following describes the response adjustment routine repeatedlycarried out by the response level determiner 47, i.e. the CPU 4 a, everyprocessing cycle using FIG. 5. In particular, the response leveldeterminer 47 executes the response adjustment routine for each of theselected boundary line candidates selected by the selector 48. In thefollowing descriptions, the selected boundary line candidate willtherefore represent any one of the selected boundary line candidates.

In step S100, the response level determiner 47 resets each of previouslyprepared determination flags F1 to F7 to zero.

In step S110, the response level determiner 47 determines whether atleast one of the first reliability DR and the second reliability DB ofthe selected boundary line candidate calculated by the informationextractor 46 is less than a predetermined reliability threshold. Upondetermination that at least one of the first reliability DR and thesecond reliability DB is less than the predetermined reliabilitythreshold (YES in step S110), the response level determiner 47determines that there is an unstable situation in which the reliability,i.e. the line reliability, of the selected boundary line candidate beinga white line is low. This results in the response adjustment routineproceeding to step S120.

Otherwise, neither the first reliability DR nor the second reliabilityDB are less than the predetermined reliability threshold (NO in stepS110), the response level determiner 47 determines that the reliabilityof the selected boundary line candidate being a white line is high. Thisresults in the response adjustment routine proceeding to step S130.

In step S120, the response level determiner 47 sets the determinationflag F1 to 1, resulting in the response adjustment routine proceeding tostep S130.

In step S130, the response level determiner 47 determines whether thereis at least one high-reliability marking line in an allowable rangepreviously determined for the selected boundary line candidate. Thehigh-reliability marking line represents a boundary line candidate whosefirst and second reliabilities DR and DB are each equal to or more thanthe corresponding one of the reliability thresholds. In addition, theallowable range for the selected boundary line candidate is defined as apredetermined shaped margin, such as a rectangular shaped margin, aroundthe outline of the selected boundary line candidate with a commondistance D relative to the outline of the selected boundary linecandidate (see FIG. 6A). The rectangular shaped margin with the commonmaximum distance D is defined such that edge points located within thearea surrounded by the rectangular shaped margin are regarded toconstitute the selected boundary line candidate.

Upon determination that there is at least one high-reliability markingline in the allowable range previously determined for the selectedboundary line candidate (YES in step S130), the response leveldeterminer 47 determines that there is an unstable situation in whichthe selected boundary line candidate and the at least onehigh-reliability marking line may be confused with each other. Thisresults in the response adjustment routine proceeding to step S140.

Otherwise, upon determination that there are no high-reliability markinglines in the allowable range previously determined for the selectedboundary line candidate (NO in step S130), the response adjustmentroutine proceeds to step S150.

In step S140, the response level determiner 47 sets the determinationflag F2 to 1, resulting in the response adjustment routine proceeding tostep S150.

In step S150, the response level determiner 47 determines whether thevalue of at least one of the feature information items generated by theinformation extractor 46 is outside a predetermined acceptable range forthe corresponding at least one of the feature information items. Forexample, the response level determiner 47 determines whether the averagePL of the segment lengths L_(i) to L_(m) is outside a threshold lengthpredetermined under laws and regulations, or the average PW of thesegment intervals W₁ to WL_(m) is outside a threshold intervalpredetermined under laws and regulations. Each of the threshold lengthand interval serves as an example of the acceptable range.

Upon determination that the value of at least one of the featureinformation items generated by the information extractor 46 is outsidethe predetermined acceptable range for the corresponding at least one ofthe feature information items (YES in step S150), the response leveldeterminer 47 determines that there is an unstable situation in whichthe reliability of the selected boundary line candidate is low. Thisresults in the response adjustment routine proceeding to step S160.

Otherwise, upon determination that the value of each of the featureinformation items generated by the information extractor 46 is notoutside the predetermined acceptable range for the corresponding one ofthe feature information items (NO in step S150), the response adjustmentroutine proceeds to step S170.

In step S160, the response level determiner 47 sets the determinationflag F3 to 1, resulting in the response adjustment routine proceeding tostep S170.

In step S170, the response level determiner 47 determines whether atleast one of the variance VL of the segment lengths L_(i) to L_(m) andthe variance VW of the segment intervals W₁ to WL_(m) is equal to ormore than a predetermined variation threshold. Upon determination thatat least one of the variance VL of the segment lengths L₁ to L_(m) andthe variance VW of the segment intervals W₁ to WL_(m) is equal to ormore than the predetermined variation threshold (YES in step S170), theresponse level determiner 47 determines that the segments included inthe selected boundary line candidate are nonuniformly distributed, andtherefore determines that there is an unstable situation in which thereliability of the selected boundary line candidate being a marking lineis low. This results in the response adjustment routine proceeding tostep S180.

Otherwise, upon determination that neither the variance VL of thesegment lengths L_(i) to L_(m) nor the variance VW of the segmentintervals W₁ to WL_(m) is equal to or more than the predeterminedvariation threshold (NO in step S170), the response adjustment routineproceeds to step S190.

In step S180, the response level determiner 47 sets the determinationflag F4 to 1, resulting in the response adjustment routine proceeding tostep S190.

In step S190, the response level determiner 47 determines whether atleast one of the deviation ΔPL of the average PL of the segment lengthsL_(i) to L_(m) and the deviation ΔPW of the average PW of the segmentintervals W₁ to W_(m) between the current processing cycle and the lastprevious processing cycle is equal to or more than a predeterminedsudden change threshold. Upon determination that at least one of thedeviation ΔPL of the average PL of the segment lengths L₁ to L_(m) andthe deviation ΔPW of the average PW of the segment intervals W₁ to W_(m)between the current processing cycle and the last previous processingcycle is equal to or more than the predetermined sudden change threshold(YES in step S190), the response level determiner 47 determines thatthere is an unstable situation in which the value of at least one of thedeviation ΔPL and the deviation ΔPW as one of the feature informationitems has been suddenly changed due to any disturbance. This results inthe response adjustment routine proceeding to step S200.

Otherwise, upon determination that neither the deviation ΔPL of theaverage PL of the segment lengths L₁ to L_(m) nor the deviation ΔPW ofthe average PW of the segment intervals W₁ to W_(m) between the currentprocessing cycle and the last previous processing cycle is equal to ormore than the predetermined sudden change threshold (NO in step S190),the response level determiner 47 determines that the selected boundaryline candidate is unlikely to have influenced on any disturbance. Thisresults in the response adjustment routine proceeding to step S210.

In step S200, the response level determiner 47 sets the determinationflag F5 to 1, resulting in the response adjustment routine proceeding tostep S210.

In step S210, the response level determiner 47 determines whether thetype of the selected boundary line candidate identified from theinformation extractor 46 in the last previous processing cycle has beenchanged to another type in the current processing cycle. In other words,in step S210, the response level determiner 47 determines whether theline type identification flag has been set to 1. Upon determination thatthe line type identification flag has been set to 1 (YES in step S210),the response level determiner 47 determines that there is an unstablesituation in which the identified result of the type of the selectedline boundary candidate by the information extractor 46 is varied due toany disturbance. This results in the response adjustment routineproceeding to step S220.

Otherwise, upon determination that the line type identification flag hasbeen maintained at 0 (NO in step S210), the response adjustment routineproceeds to step S230.

In step S220, the response level determiner 47 sets the determinationflag F6 to 1, resulting in the response adjustment routine proceeding tostep S230.

In step S230, the response level determiner 47 determines whether theluminance deviation ΔC of the luminance level obtained by the currentprocessing cycle from the luminance level obtained by the last previousprocessing cycle, which is generated by the ambient informationobtaining unit 43, is equal to or more than a predetermined luminancethreshold. The luminance threshold can be set to a level that enableschange between the average luminance level of all the pixels in oneimage and the average luminance level of all the pixels in the nextimage generated while the own vehicle V is passing through a shaderegion, such as the region under an elevated structure, to be detected.

Upon determination that the luminance deviation ΔC of the luminancelevel obtained by the current processing cycle from the luminance levelobtained by the last previous processing cycle is equal to or more thanthe predetermined luminance threshold (YES in step S230), the responselevel determiner 47 determines that there is an unstable situation inwhich the marking-line recognition environment has been suddenly changeddue to, for example, entrance of the own vehicle V into tunnel or aregion under an elevated structure. This results in the responseadjustment routine proceeding to step S240.

Otherwise, upon determination that the luminance deviation ΔC of theluminance level obtained by the current processing cycle from theluminance level obtained by the last previous processing cycle is lessthan the predetermined luminance threshold (NO in step S230), theresponse level determination routine proceeds to step S250.

In step S240, the response level determiner 47 sets the determinationflag F7 to 1, resulting in the response adjustment routine proceeding tostep S250.

In step S250, the response level determiner 47 determines the responselevel of the Kalman filter KF as a function of at least one of thevalues of the flags F1 to F7, the speed of the own vehicle V, and theyaw rate of the own vehicle V. Thereafter, the response level determiner47 terminates the response adjustment routine.

For example, the response level determiner 47 determines, in step S250,the response level of the Kalman filter KF to thereby reduce theresponse level of the Kalman filter KF stored in the memory 4 b if atleast one of the values of the flags F1 to F7 is set to 1. In otherwords, the response level determiner 47 maintains, in step S250, theresponse level of the Kalman filter KF to be high until at least one ofthe values of the flags F1 to F7 is changed from 0 to 1.

As another example, in step S250, the response level determiner 47determines the response level of the Kalman filter KF to thereby reducethe response level of the Kalman filter KF with an increase of thenumber of the values of the flags F1 to F7 being set to 1 or with anincrease of the speed of the own vehicle V or with a decrease of the yawrate of the own vehicle V. In other words, the response level determiner47 adjusts the response level of the Kalman filter KF such that, thelarger the number of the values of the flags F1 to F7 being set to 1 is,or the faster the speed of the own vehicle V is, or the lower the yawrate of the own vehicle V is, the lower the response level of the Kalmanfilter KF is.

As a further example, the response level determiner 47 weights thevalues of the flags F1 to F7, and calculates the sum of the weightedvalues of the flags F1 to F7. Then, the response level determiner 47adjusts the response level of the Kalman filter KF such that, the largerthe calculated sum of the weighted values of the flags F1 to F7 is, thelower the response level of the Kalman filter KF is.

In addition, the response level determiner 47 can change the responselevel of the Kalman filter KF as a function of

(1) The first reliability DR of the selected boundary line candidate

(2) The second reliability DB of the selected boundary line candidate

(3) The values of the feature information items obtained by theinformation extractor 46

(4) The variations of the values of the feature information itemsobtained by the information extractor 46

(5) The degree of sudden change of at least one of the featureinformation items obtained by the information extractor 46

(6) The degree of sudden change of the luminance level around the ownvehicle V obtained by the ambient information obtaining unit 43

(7) How close the selected line boundary candidate is to the at leastone high-reliability marking line

How Road Estimating Apparatus 4 Works

Next, the following describes how the road estimating apparatus 4 works.

Let us assume a travelling situation of the own vehicle V in which aforward scene is captured as a forward image I by the image capturingunit 2 as illustrated in FIG. 6B. As illustrated in FIG. 6B, there aretire traces extending along a left boundary line, i.e. left white dashedline, LI1 to be adjacent to the left boundary line LI1 on a road, andthere is a coal-tar line extending along a right boundary line, i.e. aright dashed white line, RI1 to be adjacent to the right boundary lineRI1 on the road. Let us also assume that there are no shade regions onthe road in the forward scene.

In the above assumption, FIG. 7 illustrates a luminance leveldistribution of the image I on a horizontal pixel line PL. Asillustrated in FIG. 7, the luminance levels of the respective tiretraces and the coal-tar line are lower than the luminance level of theleft boundary line LI1 and the luminance level of the right boundaryline RI1 white dashed line. This enables the first reliability DR foreach of the tire traces and the coal-tar line to be lower than the firstreliability DR for each of the left and right boundary lines LI1 andRI1. Similarly, this enables the second reliability DB for each of thetire traces and the coal-tar line to be lower than the secondreliability DR for each of the left and right boundary lines LI1 andRI1.

This enables the road estimating apparatus 4 to determine that there isan unstable situation in which the reliability of a selected boundaryline candidate based on such a tire trace or a coal-tar line being alane boundary line, i.e. a white line, is low (see step S120). Thismakes it possible to reduce the response level of the Kalman filter KFif there is an unstable situation in which the reliability of a selectedboundary line candidate based on such a tire trace or a coal-tar linebeing a lane boundary line, i.e. a white line, is low.

If there is a travelling situation of the own vehicle V passing througha shade region SR under an elevated structure, a forward scene iscaptured as a forward image I1 by the image capturing unit 2 asillustrated in FIG. 8. As illustrated in FIG. 8, the captured image I1becomes dark, because time lag of control of the exposure time of theimage capturing unit 2. At that time, if light, which is entering intothe shade region, is reflected by a coal-tar line located in the shaderegion SR, the luminance level of the coal-tar line becomes higher thanthe luminance level of a part of a boundary line LI3; the part of theboundary line LI3 is located to be adjacent to the cola-tar line withinthe shade region SR. If the coal-tar line is erroneously recognized as aboundary line, the selected boundary line candidate based on an actuallane boundary line, i.e. a dashed white line, may be switched to anerroneous selected boundary line candidate based on the coal-tar line.

If the selected boundary line candidate based on an actual lane boundaryline is switched to an erroneous selected boundary line candidate basedon the coal-tar line, the value of at least one of the featureinformation items, such as the type of the selected boundary linecandidate or the entire length AL of the selected boundary candidate issuddenly changed to another value. Similarly, if the selected boundaryline candidate based on an actual lane boundary line is switched to anerroneous selected boundary line candidate based on the coal-tar line,the value of the luminance level in front of the own vehicle V issuddenly changed to another value.

From this viewpoint, the road estimating apparatus 4 described above isconfigured to detect this sudden change of the value of the at least oneof the feature information items (see YES in at least one of steps S190and 210) or this sudden change of the value of the luminance level infront of the own vehicle V (see YES in step S230). This enables the roadestimating apparatus 4 to determine that there is an unstable situationin which the marking-line recognition environment has been suddenlychanged due to, for example, entrance of the own vehicle V into tunnelor a region under an elevated structure. This makes it possible toreduce the response level of the Kalman filter KF if there is anunstable situation in which the marking-line recognition environment hasbeen suddenly changed due to, for example, entrance of the own vehicle Vinto tunnel or a region under an elevated structure.

As illustrated in FIG. 9A, there is a group of coal-tar lines extendingalong a lane boundary line LI4 to be adjacent to the lane boundary lineLI4 on the road in front of the own vehicle V. In addition, asillustrated in FIG. 9B, there is a group of tire traces extending alonga lane boundary line LI5 to be adjacent to the lane boundary line LI5 onthe road in front of the own vehicle V.

As illustrated in FIG. 7, the luminance levels of the respective groupof coal-tar lines and the group of tire traces are lower the luminancelevel of the road surface. This may result in such a low luminance levelportion (the group of coal-tar lines or the group of tire traces) beingerroneously recognized as the road surface, and the actual road surfacebeing erroneously recognized as a lane boundary line.

However, because the luminance levels of the line segments of the roadsurface, which are erroneously recognized as a lane boundary line, aresimilar to the reference luminance level, the first reliability DR ofthe line segments becomes lower. In addition, because the luminancelevels of the line segments of the low luminance level portion, such asthe group of coal-tar lines or the group of tire traces, which iserroneously recognized as the road surface, are lower than the referenceluminance level, the second reliability DB of the low luminance levelportion becomes lower.

That is, the road estimating apparatus 4 makes it harder for theselector 48 to select such a low luminance level portion, such as thegroup of coal-tar lines or the group of tire traces, as a lane boundarycandidate.

Additionally, if such a low luminance level portion, such as the groupof coal-tar lines or the group of tire traces, as a lane boundarycandidate, is selected as a lane boundary candidate, it is possible todetermine that there is an unstable situation in which the reliabilityof a selected boundary line candidate based on the low luminance levelportion being a lane boundary line, i.e. a white line, is low (see stepS120).

This makes it possible to reduce the response level of the Kalman filterKF if there is an unstable situation in which the reliability of aselected boundary line candidate based on such a tire trace or acoal-tar line being a lane boundary line, i.e. a white line, is low.This results in reduction of adverse effects of disturbance due tocoal-tar lines and/or tire traces on the accuracy of the road estimationroutine, i.e. accuracy of the estimated travelling road of the ownvehicle V.

Advantageous Effect

As described in detail above, the exemplary embodiment obtains thefollowing advantageous effects.

The road estimating apparatus 4 is configured to maintain the responselevel of the Kalman filter KF to be high until it is determined thatthere is an unstable situation that causes the accuracy of extractingthe edges to be reduced. This configuration ensures the high responselevel of the Kalman filter KF independently of whether the own vehicle Vis travelling toward a straight section of a road or a curved section inthe road. For example, the road estimating apparatus 4 maintains thehigh response level of the Kalman filter KF before measurement of theyaw rate of the own vehicle V upon the own vehicle V being entering acurve in the road.

The road estimating apparatus 4 is configured to reduce the responselevel of the Kalman filter KF when it is determined that there is anunstable situation that causes the accuracy of extracting the edges tobe reduced, i.e. the accuracy of recognizing lane marking lines to bereduced. This enables the stability of estimating the state values ofthe travelling road of the own vehicle V to be improved, making itpossible to inhibit the lane keeping assist control based on theestimated state values of the travelling road to be more stable.

FIG. 10 illustrates

(1) The first result of the curvature of a straight road includingcracks on road estimated by the conventional technology disclosed in thepublished patent document, in which the response level of a Kalmanfilter is kept unchanged while a corresponding vehicle is travelling onthe straight road, using a dashed line

(2) The second result of the curvature of the same road estimated by theroad estimating apparatus 4 while the own vehicle V is travelling on thesame road using a solid line

The first result estimated by the conventional technology shows that,upon the corresponding vehicle travelling on a road with cracks, thecracks may be erroneously recognized as lane boundary lines, resultingin the estimated result of the curvature of the road becoming unstable.

In contrast, the second result estimated by the road estimatingapparatus 4 shows that, even if the own vehicle 4 is travelling on thesame road with the cracks, the road estimating apparatus 4 prevents thecracks from being erroneously recognized as lane boundary lines,resulting in unstable estimation of the curvature of the road withoutsudden change of the curvature of the road.

Modification

The exemplary embodiment of the present disclosure has been describedabove. The present disclosure is however not limited to the exemplaryembodiment, and can be variably modified.

The response level determiner 47 determines that there is an unstablesituation for recognizing the selected boundary line candidate as a laneboundary line upon determination that there is at least onehigh-reliability marking line, i.e. another boundary line candidate, inthe allowable range previously determined for the selected boundary linecandidate. The present disclosure is however not limited to theconfiguration.

Specifically, the response level determiner 47 can be configured todetermine that there is an unstable situation for recognizing theselected boundary line candidate as a lane boundary line upondetermination that there are many edge points due to any disturbance,such as coal-tar lines, in the allowable range previously determined forthe selected boundary line candidate; these edge points are notextracted as a boundary line candidate (see FIG. 11).

The estimator 49 estimates the road parameters using the Kalman filterKF, but the present disclosure is not limited to the configuration.Specifically, the estimator 49 can include another filter, such as anH_(∞) filter, i.e. an H-infinity filter, used to estimate the variablesof a state estimation model. The response level determiner 47 can beconfigured to adjust a response level of another filter in the samemanner as adjusting the response level of the Kalman filter KF.

The functions of one element in the above embodiment can be distributedas plural elements, and the functions that plural elements have can becombined into one element. At least part of the structure of the aboveembodiment can be replaced with a known structure having the samefunction as the at least part of the structure of the embodiment. A partof the structure of the above embodiment can be eliminated. All aspectsincluded in the technological ideas specified by the language employedby the claims constitute embodiments of the present disclosure.

The present disclosure can be implemented by various embodiments inaddition to the road estimating apparatus; the various embodimentsinclude systems each including the road estimating apparatus, programsfor serving a computer as the road estimating apparatus, non-transitorystorage media storing the programs, and road estimating methods.

While the illustrative embodiment and its modifications of the presentdisclosure have been described herein, the present disclosure is notlimited to the embodiments and their modifications described herein.Specifically, the present disclosure includes any and all embodimentshaving modifications, omissions, combinations (e.g., of aspects acrossvarious embodiments), adaptations and/or alternations as would beappreciated by those in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application,which examples are to be construed as non-exclusive.

What is claimed is:
 1. A travelling road estimating apparatuscomprising: an edge extractor configured to: extract edge points from animage of a travelling road ahead of a vehicle captured by an imagecapturing unit of the vehicle; and calculate coordinates of each of theedge points, each of the edge points representing an extracted pixel ofthe image, the extracted pixel having a luminance level higher by atleast a predetermined threshold level than a luminance level of at leastone of pixels of the image adjacent to the extracted pixel; a candidateextractor configured to extract, based on the coordinates of each of theedge points, at least one line candidate that is a candidate of a lanemarking line of the travelling road; a selector configured to select theat least one line candidate as a selected candidate; an estimatorconfigured to estimate, based on the coordinates of at least one of theedge points included in the selected candidate, a road parameter using apreviously prepared filter having an adjustable response level, the roadparameter being associated with a condition of the travelling roadrelative to the vehicle and a shape of the travelling road; a determinerconfigured to determine whether there is an unstable situation thatcauses an accuracy of estimating the edge points by the edge extractorto be reduced; and a response level adjuster configured to adjust theresponse level of the filter in accordance with determination of whetherthere is an unstable situation that causes an accuracy of estimating theedge points by the edge extractor to be reduced.
 2. The travelling roadestimating apparatus according to claim 1, further comprising: areliability calculator configured to calculate a reliability of the atleast one line candidate selected as the selected candidate, thereliability of the selected candidate taking a value being higher as theselected candidate is more similar to the lane marking line, wherein thedeterminer is configured to: determine whether the calculatedreliability is lower than a predetermined reliability threshold; anddetermine that there is the unstable situation upon determination thatthe calculated reliability is lower than the predetermined reliabilitythreshold.
 3. The travelling road estimating apparatus according toclaim 2, wherein: the reliability calculator is configured to: set thereliability of the selected candidate to be higher with an increase ofat least one of a first contrast and a second contrast, the firstcontrast being a contrast ratio of a luminance level of a lineidentified from the selected candidate to an averaged luminance level ofa road-surface portion of the image, the second contrast being acontrast ratio of a color of the line identified from the selectedcandidate to an averaged color of the road-surface portion of the image.4. The travelling road estimating apparatus according to claim 2,wherein: the reliability calculator is configured to: set thereliability of the selected candidate to be higher with a decrease of atleast one of a first contrast and a second contrast, the first contrastbeing a contrast ratio of a luminance level of a road-surface portion ofthe image at a boundary of a line identified from the selected candidateto an averaged luminance level of the road-surface portion of the image,the second contrast being a contrast ratio of a color of theroad-surface portion of the image at the boundary of the line identifiedfrom the selected candidate to an averaged color of the road-surfaceportion of the image.
 5. The travelling road estimating apparatusaccording to claim 2, wherein: the at least one line candidate extractedby the candidate extractor comprises a plurality of line candidates; thereliability calculator is configured to calculate the reliability ofeach of the line candidates, at least one of the line candidates beingselected as the selected candidate; and the determiner is configured to:determine whether there is at least one low-reliability line candidatein the line candidates within an allowable range defined around theselected candidate, the reliability of the at least one low-reliabilityline candidate being lower than a predetermined threshold; and determinethat there is the unstable situation upon determination that there is atleast low-reliability candidate in the line candidates within theallowable range defined around the selected candidate.
 6. The travellingroad estimating apparatus according to claim 1, further comprising: afeature generator configured to generate an item of feature informationindicative of a feature of a line identified from the selectedcandidate, wherein the determiner is configured to: determine whetherthe generated item of the feature information is outside a predeterminedacceptable range; and determine that there is the unstable situationupon determination that the generated item of the feature information isoutside a predetermined acceptable range.
 7. The travelling roadestimating apparatus according to claim 6, wherein: the featuregenerator is configured to, if the line identified from the selectedcandidate is comprised of a plurality of line segments, generate, as theitem of the feature information, an item of the feature information foreach of the line segments, the feature generator being configured tocalculate an average and a variation of the generated line segments; andthe determiner is configured to: determine whether at least one of thecalculated average and the variation is equal to or more than acorresponding at least one feature threshold; and determine that thereis the unstable situation upon determination that the at least one ofthe calculated average and the variation is equal to or more than thecorresponding at least one feature threshold.
 8. The travelling roadestimating apparatus according to claim 7, wherein: the line identifiedfrom the selected candidate is a dashed line comprised of the pluralityof line segments; and the feature generator is configured to generate,as the item of the feature information for each of the line segments, atleast one of: a longitudinal length of the corresponding one of the linesegments; and a length of each of intervals between the respectiveplurality of line segments.
 9. The travelling road estimating apparatusaccording to claim 6, wherein: the image is captured by the camera everypredetermined processing cycle; the feature generator is configured to:generate the item of the feature information every predeterminedprocessing cycle, the item of the feature information generated for afirst processing cycle in the processing cycles being referred to as afirst item, the item of the feature information generated for a secondprocessing cycle in the processing cycles being referred to as a seconditem, the second processing cycle being next to the first processingcycle; and calculate an absolute value of a deviation of the second itemof the feature information from the first item of the featureinformation; and the determiner is configured to: determine whether theabsolute value of the deviation of the second item of the featureinformation from the first item of the feature information is equal toor more than a predetermined sudden-change threshold; and determine thatthere is the unstable situation upon determination that the absolutevalue of the deviation of the second item of the feature informationfrom the first item of the feature information is equal to or more thanthe predetermined sudden-change threshold.
 10. The travelling roadestimating apparatus according to claim 9, wherein: the featuregenerator is configured to generate, as the item of the featureinformation every predetermined processing cycle, a type of the lineidentified from the selected candidate, the type of the line identifiedfrom the selected candidate for a first processing cycle in theprocessing cycles being referred to as a first line type, the type ofthe feature information generated for a second processing cycle in theprocessing cycles being referred to as a second line type, the secondprocessing cycle being next to the first processing cycle; and thedeterminer is configured to: determine whether the second line type hasbeen changed from the first line type; and determine that there is theunstable situation upon determination that the second line type has beenchanged from the first line type.
 11. The travelling road estimatingapparatus according to claim 1, wherein: the image is captured by thecamera every predetermined processing cycle, the travelling roadestimating apparatus further comprising: an ambient informationgenerator configured to: obtain a luminance level around the road aheadof the vehicle every predetermined processing cycle, the luminance levelaround the road ahead of the vehicle for a first processing cycle in theprocessing cycles being referred to as a first luminance level, theluminance level around the road ahead of the vehicle for a secondprocessing cycle in the processing cycles being referred to as a secondluminance level, the second processing cycle being next to the firstprocessing cycle; and calculate an absolute value of a luminancedeviation of the second luminance level from the first luminance level,wherein the determiner is configured to: determine whether the generatedabsolute value of the luminance deviation is equal to or more than apredetermined luminance threshold; and determine that there is theunstable situation upon determination that the absolute value of theluminance deviation is equal to or more than the predetermined luminancethreshold.
 12. The travelling road estimating apparatus according toclaim 2, wherein: the response level adjuster is configured to change adegree of adjustment of the response level of the filter as a functionof at least one of a speed of the vehicle and the reliability of theselected candidate.
 13. The travelling road estimating apparatusaccording to claim 1, wherein the estimator is configured to estimatethe road parameter using a Kalman filter as the previously preparedfilter.
 14. A travelling road estimating method comprising: extractingedge points from an image of a travelling road ahead of a vehiclecaptured by an image capturing unit of the vehicle; calculatingcoordinates of each of the edge points, each of the edge pointsrepresenting an extracted pixel of the image, the extracted pixel havinga luminance level higher by at least a predetermined threshold levelthan a luminance level of at least one of pixels of the image adjacentto the extracted pixel; extracting, based on the coordinates of each ofthe edge points, at least one line candidate that is a candidate of alane marking line of the travelling road; selecting the at least oneline candidate as a selected candidate; estimating, based on thecoordinates of at least one of the edge points included in the selectedcandidate, a road parameter using a previously prepared filter having anadjustable response level, the road parameter being associated with acondition of the travelling road relative to the vehicle and a shape ofthe travelling road; determining whether there is an unstable situationthat causes an accuracy of estimating the edge points by the extractingstep to be reduced; and adjusting the response level of the filter inaccordance with determination of whether there is an unstable situationthat causes an accuracy of estimating the edge points by the edge-pointextracting step to be reduced.
 15. The travelling road estimating methodaccording to claim 14, further comprising: calculating a reliability ofthe at least one line candidate selected as the selected candidate, thereliability of the selected candidate taking a value being higher as theselected candidate is more similar to the lane marking line, wherein thedetermining step comprises: determining whether the calculatedreliability is lower than a predetermined reliability threshold; anddetermining that there is the unstable situation upon determination thatthe calculated reliability is lower than the predetermined reliabilitythreshold.
 16. The travelling road estimating method according to claim15, wherein: the calculating step sets the reliability of the selectedcandidate to be higher with an increase of at least one of a firstcontrast and a second contrast, the first contrast being a contrastratio of a luminance level of a line identified from the selectedcandidate to an averaged luminance level of a road-surface portion ofthe image, the second contrast being a contrast ratio of a color of theline identified from the selected candidate to an averaged color of theroad-surface portion of the image.
 17. The travelling road estimatingmethod according to claim 15, wherein: the calculating step sets thereliability of the selected candidate to be higher with a decrease of atleast one of a first contrast and a second contrast, the first contrastbeing a contrast ratio of a luminance level of a road-surface portion ofthe image at a boundary of a line identified from the selected candidateto an averaged luminance level of the road-surface portion of the image,the second contrast being a contrast ratio of a color of theroad-surface portion of the image at the boundary of the line identifiedfrom the selected candidate to an averaged color of the road-surfaceportion of the image.
 18. The travelling road estimating methodaccording to claim 15, wherein: the at least one line candidateextracted by the candidate extracting step comprises a plurality of linecandidates; the calculating step calculates the reliability of each ofthe line candidates, at least one of the line candidates being selectedas the selected candidate; and the determining step comprises:determining whether there is at least one low-reliability line candidatein the line candidates within an allowable range defined around theselected candidate, the reliability of the at least one low-reliabilityline candidate being lower than a predetermined threshold; anddetermining that there is the unstable situation upon determination thatthere is at least low-reliability candidate in the line candidateswithin the allowable range defined around the selected candidate. 19.The travelling road estimating method according to claim 14, furthercomprising: generating an item of feature information indicative of afeature of a line identified from the selected candidate, wherein thedetermining step comprises: determining whether the generated item ofthe feature information is outside a predetermined acceptable range; anddetermining that there is the unstable situation upon determination thatthe generated item of the feature information is outside a predeterminedacceptable range.
 20. The travelling road estimating method according toclaim 19, wherein: the generating step comprises: generating, if theline identified from the selected candidate is comprised of a pluralityof line segments, an item of the feature information for each of theline segments as the item of the feature information; and calculating anaverage and a variation of the generated line segments; and thedetermining step comprises: determining whether at least one of thecalculated average and the variation is equal to or more than acorresponding at least one feature threshold; and determining that thereis the unstable situation upon determination that the at least one ofthe calculated average and the variation is equal to or more than thecorresponding at least one feature threshold.
 21. The travelling roadestimating method according to claim 20, wherein: the line identifiedfrom the selected candidate is a dashed line comprised of the pluralityof line segments; and the generating step generates, as the item of thefeature information for each of the line segments, at least one of: alongitudinal length of the corresponding one of the line segments; and alength of each of intervals between the respective plurality of linesegments.
 22. The travelling road estimating method according to claim19, wherein: the image is captured by the camera every predeterminedprocessing cycle; the generator step comprises: generating the item ofthe feature information every predetermined processing cycle, the itemof the feature information generated for a first processing cycle in theprocessing cycles being referred to as a first item, the item of thefeature information generated for a second processing cycle in theprocessing cycles being referred to as a second item, the secondprocessing cycle being next to the first processing cycle; andcalculating an absolute value of a deviation of the second item of thefeature information from the first item of the feature information; andthe determining step comprises: determining whether the absolute valueof the deviation of the second item of the feature information from thefirst item of the feature information is equal to or more than apredetermined sudden-change threshold; and determining that there is theunstable situation upon determination that the absolute value of thedeviation of the second item of the feature information from the firstitem of the feature information is equal to or more than thepredetermined sudden-change threshold.
 23. The travelling roadestimating method according to claim 22, wherein: the generating stepgenerates, as the item of the feature information every predeterminedprocessing cycle, a type of the line identified from the selectedcandidate, the type of the line identified from the selected candidatefor a first processing cycle in the processing cycles being referred toas a first line type, the type of the feature information generated fora second processing cycle in the processing cycles being referred to asa second line type, the second processing cycle being next to the firstprocessing cycle; and the determining step comprises: determiningwhether the second line type has been changed from the first line type;and determining that there is the unstable situation upon determinationthat the second line type has been changed from the first line type. 24.The travelling road estimating method according to claim 14, wherein:the image is captured by the camera every predetermined processingcycle, the travelling road estimating method further comprising: anambient information generating step including: obtaining a luminancelevel around the road ahead of the vehicle every predeterminedprocessing cycle, the luminance level around the road ahead of thevehicle for a first processing cycle in the processing cycles beingreferred to as a first luminance level, the luminance level around theroad ahead of the vehicle for a second processing cycle in theprocessing cycles being referred to as a second luminance level, thesecond processing cycle being next to the first processing cycle; andcalculating an absolute value of a luminance deviation of the secondluminance level from the first luminance level, wherein the determiningstep comprises: determining whether the generated absolute value of theluminance deviation is equal to or more than a predetermined luminancethreshold; and determining that there is the unstable situation upondetermination that the absolute value of the luminance deviation isequal to or more than the predetermined luminance threshold.
 25. Thetravelling road estimating method according to claim 15, wherein: theadjusting step changes a degree of adjustment of the response level ofthe filter as a function of at least one of a speed of the vehicle andthe reliability of the selected candidate.
 26. The travelling roadestimating method according to claim 14, wherein the estimating stepestimates the road parameter using a Kalman filter as the previouslyprepared filter.